DP-600 Detailed Study Plan, New DP-600 Exam Questions

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Microsoft DP-600 Exam Syllabus Topics:

TopicDetails
Topic 1
  • Implement and manage semantic models: This section of the exam measures the skills of architects and focuses on designing and optimizing semantic models to support enterprise-scale analytics. It evaluates understanding of storage modes and implementing star schemas and complex relationships, such as bridge tables and many-to-many joins. Architects must write DAX-based calculations using variables, iterators, and filtering techniques. The use of calculation groups, dynamic format strings, and field parameters is included. The section also includes configuring large semantic models and designing composite models. For optimization, candidates are expected to improve report visual and DAX performance, configure Direct Lake behaviors, and implement incremental refresh strategies effectively.
Topic 2
  • Prepare data: This section of the exam measures the skills of engineers and covers essential data preparation tasks. It includes establishing data connections and discovering sources through tools like the OneLake data hub and the real-time hub. Candidates must demonstrate knowledge of selecting the appropriate storage type—lakehouse, warehouse, or eventhouse—depending on the use case. It also includes implementing OneLake integrations with Eventhouse and semantic models. The transformation part involves creating views, stored procedures, and functions, as well as enriching, merging, denormalizing, and aggregating data. Engineers are also expected to handle data quality issues like duplicates, missing values, and nulls, along with converting data types and filtering. Furthermore, querying and analyzing data using tools like SQL, KQL, and the Visual Query Editor is tested in this domain.
Topic 3
  • Maintain a data analytics solution: This section of the exam measures the skills of administrators and covers tasks related to enforcing security and managing the Power BI environment. It involves setting up access controls at both workspace and item levels, ensuring appropriate permissions for users and groups. Row-level, column-level, object-level, and file-level access controls are also included, alongside the application of sensitivity labels to classify data securely. This section also tests the ability to endorse Power BI items for organizational use and oversee the complete development lifecycle of analytics assets by configuring version control, managing Power BI Desktop projects, setting up deployment pipelines, assessing downstream impacts from various data assets, and handling semantic model deployments using XMLA endpoint. Reusable asset management is also a part of this domain.

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Microsoft Implementing Analytics Solutions Using Microsoft Fabric Sample Questions (Q175-Q180):

NEW QUESTION # 175
You have a Fabric tenant that contains a lakehouse named LH1 and a notebook.
You have a Parquet file named invoice1 that contains a column named InvoiceDateKey of the timestamp data type.
You need to create a PySpark script that will import invoice1 and create a table named fact_sale in LH1. The solution must meet the following requirements:
* A new column named Year must be added to fact_sale.
* The values in Year must use the InvoiceDateKey column.
How should you complete the script? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

Answer:

Explanation:

Explanation:


NEW QUESTION # 176
You have a Fabric tenant that contains a data pipeline.
You need to ensure that the pipeline runs every four hours on Mondays and Fridays.
To what should you set Repeat for the schedule?

Answer: B

Explanation:
You should set Repeat for the schedule to Weekly (C). This allows you to specify the pipeline to run on specific days of the week, in this case, every four hours on Mondays and Fridays. Reference = Scheduling options for data pipelines are available in the Azure Data Factory documentation, which includes details on configuring recurring triggers.


NEW QUESTION # 177
You have a Fabric tenant that contains a Microsoft Power BI report.
You are exploring a new semantic model.
You need to display the following column statistics:
- Count
- Average
- Null count
- Distinct count
- Standard deviation
Which Power Query function should you run?

Answer: D

Explanation:
The Table.Profile function provides an overview of the data in a table, including basic statistics like count, average, null count, distinct count, and standard deviation for numerical columns.


NEW QUESTION # 178
Note: This question is part of a series of questions that present the same scenario. Each question in the series contains a unique solution that might meet the stated goals. Some question sets might have more than one correct solution, while others might not have a correct solution.
After you answer a question in this section, you will NOT be able to return to it. As a result, these questions will not appear in the review screen.
You have a Fabric tenant that contains a new semantic model in OneLake.
You use a Fabric notebook to read the data into a Spark DataFrame.
You need to evaluate the data to calculate the min, max, mean, and standard deviation values for all the string and numeric columns.
Solution: You use the following PySpark expression: df.show()
Does this meet the goal?

Answer: A


NEW QUESTION # 179
You have a Fabric tenant that contains a lakehouse named Lakehouse1. Lakehouse1 contains a Delta table that has one million Parquet files.
You need to remove files that were NOT referenced by the table during the past 30 days. The solution must ensure that the transaction log remains consistent, and the ACID properties of the table are maintained.
What should you do?

Answer: D

Explanation:
To remove files from a Delta table that are no longer referenced by the current version of the table, you should run the VACUUM command.
https://learn.microsoft.com/en-us/fabric/data-engineering/lakehouse-table-maintenance


NEW QUESTION # 180
......

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